Improving performance of a boiler-turbine unit is of interest to the energy industry due to increasing fuel costs. The system performance depends on the accuracy of models and the selected performance metrics.
Performance optimization of a boiler-turbine system is usually considered in two phases. The first is the design and implementation of a control system before the power plant becomes operational. The second is the use of the performance test code (e.g., American Society of Mechanical Engineers (ASME) performance test code) to periodically evaluate the system performance to update the operating parameters (set points) of the controllers. Kuprianov [13] discussed different objective functions to improve boiler thermal efficiency and reduce emissions based on certain test codes (or “a test code”). Farhad et al. [10] demonstrated the use of the ASME performance test code in reducing fuel and energy consumption.
Numerous modeling approaches of boiler-turbine systems have focused on using the first principle, e.g., thermodynamics. Researchers applied energy and material balance, material flow, and chemistry to derive models in the form of differential equations. Typical benchmark nonlinear models of boilers and turbines can be found in [2], [3], [8], and [22]. Ben-Abdennour and Lee [5] reported test results of a fuzzy fault accommodation controller. Moon and Lee [20] presented a fuzzy controller that can update the fuzzy rules adaptively by a simple set-point error-checking process. Espinosa et al. [9] applied fuzzy logic to identify the boiler-turbine system and implemented it to reduce overshooting and settling time. Yu and Xu [31] discussed the feasibility and efficacy of applying a feedback linearization technique to a nonlinear boiler-turbine model for control of steam pressure and electricity output. Tan et al. [28] attempted to determine control settings where distances between the nonlinear system and its corresponding linearization model were minimal; thus, the linear controller's performance was guaranteed.
Other applications of boiler-turbine control can be found in [17], [18], and [23]. The results published in the literature are not based on benchmark nonlinear boiler-turbine models. Fuzzy logic and autotuning techniques were used by [17]. A model predictive control approach [24] was illustrated in the papers by [18] and [23]. Such a technique generally uses an accurate model to predict the system behavior based on the changing inputs, and calls for the continuous solving of a quadratic programming optimization problem.
Although the literature reports progress in controlling boiler-turbine systems, the existing approaches usually are expensive to implement due to uncertainty involved in operating such systems. System errors accumulate due to the assumptions made in modeling. Also, control systems are usually designed to ensure system stability and fast response. System performance metrics, e.g., fuel consumption, are usually not well integrated in the control system. The performance test code is widely used to monitor performance; however, it involves a number of constants that are difficult to obtain, which may cause unreliable test results.
A need, therefore, exists for improved techniques for controlling boiler-turbine systems, as well as other systems, the performance of which is dependent upon non-controllable variables.